Adversarially Robust Optimization with Gaussian Processes
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Optimization is important in science and engineering as a way of finding ”optimal” situations, designs or operating conditions. Optimization is typically performed on the basis of a mathematical model of the process under investigation. In practice, optimi ...
The current context leads energy system design to very demanding objectives, due to their variety. Indeed, despite an increasing energy demand, environment indicators are becoming always more important. So that for a given service, emission (and then assoc ...
Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low norm in a reproducing ...
Institute of Electrical and Electronics Engineers2012
This work presents a synthesis method that leads to the preliminary design of industrial energy systems. Such systems are composed of several technologies that transform, through a set of physical unit operations, raw materials and energy into products and ...
Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...
The process industries are characterized by a large number of continuously operating plants, for which optimal operation is of economic importance. However, optimal operation is particularly difficult to achieve when the process model used in the optimizat ...
Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...
The paper presents an optimization design of an in-wheel BLDC motor for a kick scooter. The optimization is performed using a genetic optimization tool combined with a FEM commercial software. The new contributions of the paper are: (i) introduction of thr ...
This paper presents a method to optimize two linear actuator configurations. The method is stochastic and combines a genetic algorithm (GA) and FEM (finite element method) model generated with the commercial software FEMM. The optimization is performed in ...
An efficient hybrid method to optimize the phase states distribution, or phase diagram, of a digitally-reconfigurable reflective cell is presented. It allows minimizing phase quantization errors in applications such as reflect arrays. Digital control of th ...
